Q46.  Segmenting large sets of data into smaller segments is a common practice when analysis is done on data sets. In what different ways does segmentation relate to Root Cause Analysis? 

 

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Data segmentation is about breaking up larger data sets in to smaller ones of logical groups, based on specific parameter to help in focused analysis. Since data segmentation helps in categorizing similar data sets, It helps to focus down root causes of the problem to specific category of data, avoiding the possibility of inter influence of root causes. If different segment of data are involved in the target set, there is possibility of bias on the root causes of the problem, leading to improper diagnosis of the problem. Data segmentation also focusses on the accuracy of the data, which makes it a perfect scenario for perfect root cause analysis of the problem. Data segmentation also helps in breaking up of the complex problem in to manageable and uniform segments, facilitating effective root cause analysis.

Segmentation is the process of diving or grouping a set of large number of similar components (set of entire customer base, set of data points, set of all functional components and so on) into logical sub-groups based on some type of common or shared characteristic.

 

In Root Cause Analysis, segmentation helps to reduces (by elimination) the number of suspected process steps or potential problem spots that may be causing the issue/problem being root caused.

Successful segmentation would help in cutting out process steps and functional components that may not be involved in creating the problem.

 

Data items that can indicate the existence of the problem need to be identified and analyzed. When such data points are collected with care then irrelevant steps and components can be removed and the potential source of the problem can be isolated.

 

1. Example of reducing suspect process steps: If a physical defect or non-conformity is noticed in a finished at some stage in the process, then a visual check on subsequent products at various stages would help identify the problem process. All subsequent process can be removed from the suspicion list.
2. Example for reducing suspect functional components: In software development to identify code blocks that might be causing an issue, prompts (error messages, print statements) are used to track the logic flow and identify code blocks (functional components) that get executed without an issue. After eliminating these, the developer is left with a reduced set of components that needs to be debugged further.

Root cause analysis (RCA) is a problem solving technique used for identifying root causes for the problem. There are two types of factors for a problem. Casual factor and root cause factor.

During RCA, when we find a root cause and eliminate the root cause the problem does not arise again. On the other end if we remove the casual factor for the problem. We will get rid of the problem, but there is a risk that the problem can arise again.

It is very important that we follow proper step of root cause analysis and eliminate problem permanently.

In entire process of root cause analysis, data segmentation is very useful.

 

Considering an example,

Problem:- There are high no. of defect in product and we are getting complains and our Sigma level is affected by it.

 

Step 1:- Gather data for all complains.

Step 2:- Data segmentation based on type of product/SKU.

Step 3:- Identifying which product/SKU is having more complains.

Step 4:- Data collection of complains for that product/SKU.

Step 5:- Data segmentation of complains based on time, shift, operators, QC technicians

Step 6:- If we find that there is a particular pattern for complains based on any particular variable. We need to drill down more on that variable

Step 7:- In this example, we see that a particular trend id followed based on time. i.e during start of calendar year complains are less and it is gradually increasing after June till December.

Step 8:- Data collection for processing parameter that were used during the time and segmenting those data into two parts. Jan to May and June to Dec.

Step 9:- we derived that processing parameters for sorting machine are same for entire year. At the same time defect in incoming material was increasing.

Step 10:- on performing 5Why analysis, we found that sorting machine was capable of handling the defects in incoming material but the process recipe was not set for higher defects in incoming material.

Step 11:- Action taken that machine automatically detects that if the no. defects in incoming material is high than sorting machine changes its parameters on its own.

In the entire process, we can see that data segmentation into right variable helps us a lot in finding the root cause analysis.

The purpose of segmentation is to cut down the number of suspected process steps or sub components within a system involved in problem creation.  Successful segmentation will narrow down the investigation to fewer models by cutting out process steps,  system or components that are not involved in creating the problem.  The effort in segmentation is to find don't and learn data items that can differentiate between  segments by telling if the problem source is there  or not there. 

Narrow down the number of suspect process steps into fewer or even to one process steps that caused the problem. 

For example if a defect is caught at final stage of vehicle like water leakage then by back tracking all process steps one one by one,  we reach at a conclusion that the part from which die is producing,  problem in die profile itself that's why water leaked is there because it disturbs the part profile or in other words we can say that if once for d the suspected process step then all subsequent process step are no longer suspect. 

In segmentation natural groups are already exist and we are trying to isolate the problem by breaking it into smaller  a d smaller segments so that we can find out root cause of the problem exactly. Segmentation breaks down the data I to smaller groups.  It helps to find out root cause more precisely because it is very tedious job to collect data throughly and get something from it.  So segmentation can be done in three categories 

1 industry 

2 Company

3. Individual 

 

From three categories we have total 9 segmentation variables which segments the whole data for further analysis to find out root cause.  And all these are related to rca to gather data in a systematic manner and to do analysis in a systematic way to provide proper solution. 

Following are the segmentation variables:

1. INDUSTRY has three variable

A.  SIZE

B SUBSTITUTE

C.  SALES CYCLE

 

2.  COMPANY also has three variable 

A.  Purcahse

B.  Process

C.  People

 

3. INDIVIDUAL also has three varibale

A. Disposition

B.  Demeanor

C.  Demographic 

 

 

Segmentation means to divide the marketplace into parts, or segments, which are definable, accessible, actionable, and profitable and have a growth potential. It has been used by marketers to get a close-up view of the market.  It takes on great significance in today's cluttered marketplace, with thousands of products, media proliferation and ad-fatigue. Rightly segmenting the market place can make the difference between successes and shut down for a company. 
 

The purpose of segmentation is to cut down the number of process steps within a system involved in problem creation. Segmentation allows to closely tailor product to the needs, desires, uses and paying ability of the customers with specific characteristics. Collecting data is time consuming and tedious. By applying the principles of segmentation deep insight about problems at hand and potential solutions can be explored.

Nine segmentation variables in three categories will help perform quick and effective root cause analysis. Root cause analysis (RCA) is a method of problem solving used for identifying the root causes of faults or problems. A factor is considered a root cause if removal prevents the undesirable outcome from recurring. Root cause analysis should be performed as soon as possible after the error or variance occurs. Otherwise, important details may be missed.

1.  Industry: to uncover trends about sales cycles, size and substitutes in similar businesses like telecommunications, financial service.

2.  Company: to uncover patterns of purchasing, processes and people

3.  Individual: to uncover their personal disposition, demeanour and demographics

Evaluating different segments provide quick insight to executives and senior management. A good way to start is to probably take a look at the top and bottom 20 or top and bottom 10%. A finite workable set of information is provided where emerging patterns or gaps are easy to identify. Since the goal is to find issues quickly and effectively, the best approach is to evaluate the company related variables first, and then perform the analysis up to the industry level or down to the individual level to get detailed insight. These techniques are to be used proactively to address existing gaps and launch a product that meets business goals.

Segmentation by external factors helps to find the drivers of variation. For example for loans disbursement- categorization by size i.e. small, medium and large is segmentation. 
If there is same mean and different variances RCA can be done.
If there is different mean and the same variances segmentation has to be done after finding the causes of variation specific to each segment .Compare processes and try to equate the means. But if there is different mean and different variance then segments have to be defined and identify the segment driving the variation.

 

 

 

 

One of the very common method used for dealing with a large data is to “stratify” the data into groups. The stratification may be done in multiple ways depending upon the situation and purpose for analyzing the data. For instance, if we are studying national sales data to understand the areas having improvement opportunities, the data may be stratified into groups for each state. Other ways of stratifying may be on age, income levels, education levels, month wise etc. The stratification groups need to be decided based on the objective that is being pursued.

 

Such segmentation will help us to represent the data using a bar chart and helps comparing the variation between the groups. It helps in narrowing our focus on areas that depict an abnormal problem, or areas of opportunity.

 

During root cause analysis, such segmentation is one of the first steps adopted. It also helps in evolving a Pareto diagram and apply the 80 / 20 rule. Where deeper probing and analysis are required, it is a good idea to do the segmentation first, so that the efforts for such deeper analysis may be restricted to the volumes, shortlisted based on the segmentation.

 

Sometimes when we have a large amount of data; say for instance a product failure data for a period of six months, it would help to segment the data for certain time period, maybe month wise, and week wise. Of if we know of certain factors that we suspect to influence the failure under study, the data may be appropriately segmented to see a comparison of the failure rates between those events.

 

A good segmentation helps in optimizing the efforts spent for root cause analysis and facilitates arriving at the root cause faster.

Segmentation has traditionally been used by marketers to get an up close view of the market.  It is based on the principle of identifying variables that predict characteristics and behavior, using a mix of quantitative and qualitative approaches to categorize customers with specific characteristics. Identifying these variables and collecting the data is considered to be time consuming and tedious. However, applying the principles of segmentation can offer deep insight about problems at hand and even help come up with potential solutions.

Root Cause Analysis is a method used to identifying the core reason or cause for any problem or change observed in a Process.

The most commonly used method is asking the 5W and 2 H and arriving at the core reason by narrowing down to the start point of the variation or change that has brought about the situation in study away from normal or expected course of process. This method is practical in most of the cases where there is an requirement of immediate fact finding- more so used on “the floor” of operations.

Having said that, In any complex business process, identifying root causes of a problem can be challenging as there can be many misleading information. The best way to ensure that the root causes are identified reliably is to design the business processes, with accessible records of all data that is required to find root cause of any change in process outcome. The change said here can be for the bad or good of business- We must also be aware of root causes for changes that have impacted business positively. The best practices to ensure that RCA is convenient are :

Map the process: The first step towards creating a mature process is to document the process , so that mapping out any difference between contributing causes and root causes becomes easier.

Record all Actions: It is a must to keep track of all time to time business decisions and actions that have been implemented on the process – This aids to identify any internal factors that may have possibly contributed to the change.

Keep information of External Factors – Keep all senses active to monitor any external forces which may impact the business process including Competition, Market Trends, Consumer behaviour, Government policy etc. The bigger task here is to identify the most significant factors that can impact the business

Segment the available data- It is very important that all the metrics are segmented so that evaluation of the possibility of any of the segments contributing to the change is effectively done. This helps to expedite the elimination of potential factors that may have not contributed to the change.

Data Segmentation is the process of dividing the available data and grouping similar data together based on the chosen parameters. The said segments or groups can be used as the first layer of filter to narrow down to the core data point required- The data point we are discussing here is the root cause for a change in the process.

The purpose of segmentation in RCA is to group the identified process steps or sub components within the process which are involved in the  problem / change creation. Proper segmentation will narrow down the investigation to lesser segments by refuting the irrelevant process steps which are not involved or not influencing the problem/ change under study. Thus, as most of the irrelevant data segments are cut out the segment that is identified as the most probable source of the problem is isolated- This way a lot of time is saved from researching / analysing on each probable cause.

Example 1: When turning on a car through the ignition key- The starter spins but the Engine only coughs- There are 2 main suspect segments- the fuel system and the electrical ignition system. each could cause the problem and each contain many sub components. The best way to confront the problem is to understand which of the two segment is causing the issue. Checking whether there is a spark in the cable leading to the spark plugs will verify the functioning of various electrical system. If there is no spark at the end of the cable, it is necessary to continue to isolate the component (Smaller part of the segment) within the electrical system causing the failure- however, if there is a nice spark at the end of the cable one can suspect that the issue lies with the fuel system or in a smaller number of electrical components behind the cable.

This was a simple example to understand how segmentation of data is related to RCA

 

Example 2-  Further to the note on best practices to identify Root cause above ,Let’s consider a QSR on a high street – The problem is that the daily sales average has dipped considerably since a week’s time , as compared to trends of previous year same period and also as against the daily sales average of previous month. The “sudden” Dip could have been for the following reasons:

#1) The neighbourhood competition has reduced their prices– External Factor

#2) One of the marketing promotions at the outlet has stopped since a week.- External Factor

#3) Cricket Test match on TV has kept the customers hooked on to the live telecast and they remained home and reduced the footfalls across the week- External Factor

#4) There is a street repair work that is happening near the outlet- External Factor

#5) There are 3 new employees on the front counter where customer orders are taken- Internal Factor

#6) This week is examination time in the neighbouring schools and colleges- External Factor

#7) Some of the popular products were out of stock- Internal Factor

 

We have identified only 7 contributing factors to create the environment, but let us assume that there are many more such contributing factors- but not all of them could be the root cause. In the QSR Industry the 5 Ps play a big role in the business plan of the outlet namely: Place, Product, People , Price & Promotion.  If we try to assign the above contributors and their data based on the period of sales dip , into the 5 segments, and study the impact of sales drop from each group- we will be able to close down on the root cause as the group (segment) that contributes to maximum sales dip (Lost Opportunity)can be then isolated and further investigation can be done to hit the root cause , so from the above list of 7 contributors –

#1 is in the segment Price,

#2 & #3  is in the segment Promotion

#4 &#6 is in the segment Place

#5 is in segment People

#7 is in the segment Product

The impact of the Product group was seen to be the highest as though the other contributors did bring in drop in the footfalls – It is obvious that the same was an overall experience across the trading area. But the fact that we could not sell the popular products to even the people who did visit the outlet and missed out on opportunity , was the root cause of Sales loss- All the stakeholders in the organization who are responsible for product supply will then need to understand what was the reason for the unavailability of products and take corrective and preventive action. Thus the Segmentation in the above example helped to narrow down on the group which had an internal factor and is within the control of the organization.

In conclusion, it can be stated that the advantage of segmentation is that by targeting the right segment, the exercise of RCA becomes faster and more effective.

 

Segmentation could be called as the process of dividing the population into distinct subsets or segments that behave in the same way or have similar features. As each segment is homogeneous, they are likely to respond similarly, within. For effective segmentation, segments need to be measurable (The very purpose is to measure effects within a segment and between segments), identifiable (This is mandatory if all data is to be correctly segmented), accessible (The efforts at segmenting should not become more than the benefits obtained by solving the problem), actionable (The segments arrived at must be practically feasible to work on) and large enough to be effective (Each segment should have a critical enough mass). The segments arrived at need to be based on a logic that can be related to the problem being investigated or business goals being pursued. These questions may help drive the analysis of the segmented data.

·         Is there one defect category that occurs more frequently than others?

·         What factors contribute the most to the variation in Project Y?

·         Do results differ across factors?

 

Segmentation, sub-segmentation, cross-segmentation and matrix-segmentation divide the data population into homogeneous data segments. Multiple data segmentation can be used effectively to isolate problem transactions that give us a handle to work on solving the problem.

 

The criteria for this segmentation can be the natural transaction categories in the process or specially created criteria.

 

In the case of the former i.e. segmentation along transaction categories, the population is split into various segments and extent to which the transactions in each segment have been impacted by the problem is measured. This will help in identifying those segments which are most impacted adversely by the problem. Thus, the problem segment or segments have been identified. Then, by identifying the characteristics and features of these problem segment or segments that are significantly different from those of the other segments which are not impacted by the problem, it is possible to identify those characteristic or feature that are most impacted by the problem. These could be the immediate cause of the problem. This will then need to be root-cause analysed and appropriate controls implemented. This way, it is possible to avoid shooting in the dark when trying to find the root cause of the problem. Segmentation along transaction categories has helped to narrow down the areas to be root cause analysed, thus saving time, effort and money in the problem solving exercise.

 

In the case of the latter, i.e. segmentation along specially created criteria, it is possible to formulate criteria along suspected or potential root causes. By segmenting the data population along potential root causes, the segment or segments impacted most by the problem can be identified and along with the segments, the root causes themselves can be identified. Here, by almost directly identifying the root cause, even more savings of time, effort and money can be achieved. Here, data segmentation is actually being used to verify root causes.

 

Going further on root causes, Segmentation analysis also assists us in planning and implementing different corrective actions for different segments that contribute effectively to improvement. A repetition of the segmentation post improvements and measurement of the problem impact will reveal the effectiveness of the corrective actions implemented.

 

Thus Segmentation Analysis supports preparation for, conduct of and verifying effectiveness of root cause analysis.

Segmentation.docx

Q46.  Segmenting large sets of data into smaller segments is a common practice when analysis is done on data sets. In what different ways does segmentation relate to Root Cause Analysis? 

 

Segmentation is a useful technique used to understand the customer’s similarities and differences in the market.

It will help you to understand the current and future prospects of the products and the customer’s requirements in the market, which will in turn make the organization to introduce new products or modify the existing ones and better communication to the customer who is interested to buy the product.

It was traditionally used by the marketers in the market to get the close view of it. It is based on the principle of identifying the variables that can predict the customer’s behavior and the characteristics, using a mix of quantitative and qualitative approaches.

 

Why segmentation is useful?

1.    Segmentation provides us the deeper insight of the problems and help us identify the relevant possible solutions.

2.    It helps us to understand the customer requirements in the market, so that the company can introduce new product or modify the existing one to meet the needs of the customer.

3.    It will help you identify the real root cause by diving the entire fragment of business into categories defined.

4.    Helps in decision making, once the real root cause is identified.

 

When it is recommended?

Segmentation is recommended to focus Customer Research on the most important customers:

•          Segmentation divides customers into groups with similar needs that value the same outcomes and look for the same solutions

•          Segmentation should be natural (the way the customers actually behave) and stable over time

•          Avoid artificial segmentations such as geographic location, price point, size, vertical industry or standard industrial code that mix customers with significantly different needs

•          Write a definition of each customer segment and representative customers in the segment and review them with the sponsoring business team

 

Segmentation:

There are basic 9 segmentations which can be clubbed into 3 categories and are as follows.

1.    Industry

2.    Company

3.    Individual

 

 

1.    Industry:

It is grouping of similar businesses like healthcare, BPO, etc. the attributes identified and studied in this segment are type of business, growth & revenue, geographic and demographic locations of the company, etc along with deep dive into other important attributes like size, substitutes and sales cycle

Subcategories	Description	Example
Size	Understanding the size of the spending budge twill provide inputs to the pricing model. It will also help in understanding the challenges and overcoming it.	Some industries would be early technology adopters. In such case, the spending budgets by various industries like security software, application software, business software and internet software to be colleted and listed for pricing model.
Substitutes	Product manager should frequently communicate to the customer in terms of problem solving. If the product delivered does not meet the needs of him, then what is it he is expecting more and what alternative he is using. Such questions asked to upgrade the product or modify it within hte policy regulatory norms. Change is important but cost effective.	If the new dress is torn which is bought from the retailer A, the product can be bought from the retailer B by the customer if he is satisfied with A vendor and if he doesn't try to improve / solve the issue.
Sales Cycle	once the product lifecycle is completed, then the product sales cycle starts. It is very important that the trends to be identified basis historical data and create patterns in sales considering future deals.	If Company A sells 200 products last year, then they can study the market for product innovation and increase the sales if required.

 

Relating Industry segments to RCA:

Yes. It is related. By studying the segment of industry in terms of size, geographic location etc should really contribute to the root causes.

Eg. If a company works in metropolitan city (lets say Chennai) the traffic is huge and the IT park is as well crowded. It the person is said to come at 9 am in the morning for completing the backlog of that day’s target(200), the person is held up in traffic. When the same company also works of moderately populated a semi urban area, the person would be able to reach office on time to complete the backlog. As earlier said, the root cause for not completing the backlog is coming to office late in metropolitan city.

Tools used:

-          Pareto analysis

-          Win loss analysis

 

2.    Company:

Where in industry we would have looked in quantitative attributes and here we might look into qualitative attributes like how the company can create value proposition to the customer in terms of products or services given.

Subcategories	Description	Example
Purchasing	Companies adopt different marketing strategies like cost optimization and innovation optimization. Understanding the company's strategy will help focus on the problem areas easily and understanding the company's business goals are also equally important	A company focused on cost optimization will start focusing on the selling the products with different marketing messages  which talks about the product's innovations and its effective usage.
Process	In most of the companies, the communication is top down and some has bottom ups. It talks about how effectively the business processes work and interlinked. How effective the management can solve the problems and make decisions.	if the company adopts lean and agile, this means they need change. So the product also should facilitate the change and embrace it for 100% quality.
People	People in the company are directly involved in product life cycle and till it gets delivered to the customer. Hence it is important to understand their mindset since they are the ones involved in producing good quality products.	When we sell medicines to the customer, it is also important that we need to tell them the risks involved in taking up the medicines. Hence it should be taken only under guidance.

 

 Relating company segments to RCA:

It is obvious that the company segmentation is related to root cause anlaysis. The businsess processes involved in the company are studied using process maps and waste is identified. And process maps are used to convert it into value streams and root cause is as well identified.

Eg.

Step 1	Step 2	Step 3	Step 4	Step 5
10 minutes	20 minutes	15 minutes	10 minutes	10 minutes
Value	Waste  - can be reduced to 5 minutes using SCAR (Simplify, Combine, Automate or Remove) technique	Value	Value	Value

 

Tools used:

-          Pareto

-          Process map

-          VSM

 

3.       Individual:

Individual is nothing but your customer who purchases the product or the receiver of the service provided. Quantitative factors like demographic information can provide insight into product adoption and qualitative attributes like demeanor and disposition can provide insight into the personal motivation for product selection.

Subcategories	Description	Example
Disposition	It is important how the product add values to the customer and simplifies day's living. It is important the customer should be satisfied with their needs met by the organization when aligned with their organization’s goals, will definitely go a long way even if there are few short comings.	if the mobile is used to make calls, the camera is an additional feature. Given with it, a clarity with high pixels will delight the customer at the same price.
Demeanor	understanding this focuses on the learning process of general guidelines and the principles followed. Some may value the change and some may not. It is as important the value should be imposed in all minds through multiple learning sessions.	If the company adopts the Operational excellence, then person then strive for excellency in the products sold to the customer. It should be defect free and match the customer's needs as they fit in the organization's goals.
Demographics	Demographics like age, sex, education etc plays a major role in segmentation. Because, the roles performed In the organization also depends on the age of the generations.	A mobile with additional feature like camera clarity, unbreakable feature, voice recognitions will delight the youngsters whereas the old people will only use the phone for calling others.

 

Relations of individual with RCA:

In a medical coding company, a person with BCA computers are hired and given some charts to code. After coding is completed, another BCA guy as auditor audits and finds no error and sends it to client.

At the customer end, randomly they check 10 reports out of 100 reports submitted. They find all 10 reports to be erroneous and not acceptable.

The prime root cause is education. This is understood if the proper segmentation is done.

 

Tools used:

-          Customer feedbacks

-          Customer surveys / discussions

 

Conclusion:

This finite set of workable information provides a good insight of any problems arised. The short term goals are fixed to rectify the issues quicker. This 9 segments broadly explained will throw insights on the root causes and helps the management in decision making.

When the product is not meeting the customer’s needs, then start looking into the segmentation to find the real root cause. Understanding the key is the only best way to hone the problem. But should we have to wait for the process to fail? No. Even if the company wants to innovate the segmentation is the best tool to identify which area to be innovated.

 

Reference:

https://measuringu.com/better-segmentation/

thanks

Kavitha

 

 

 

My Perspective : After pondering over it for a while, it looks necessary that Segmentation and the Segmentation Criteria should "precede" the activity of Root Cause Analysis. It's possible that the Root Cause for a problem within one or many segments could be the same or possiblty two different Root Causes might appear for the same problem faced in 2 different Segments of Data.

Eg : Few Root Causes for lack of savings in Age Group 20-30 & 30-40 could be common. Few common Root Causes could mean that an identified Solution could address a specific root cause in either One or few or all of the Segments where the Root Cause appears. If the Identified Solution can address the similar root cause from different segments, thats a Plus/Bonus/Advantage. 

Nevertheless, RCA should not be allowed to influence the Segmentation exercise. Cannot see any merit in doing so.

Segmentation helps in understanding the data, which is required for Root Cause Analysis too. Data can be segmented into four categories:

1) Correlated Factors: these are the factors which are other symptoms of the root cause.

2) Unrelated Factors: these factors are not related to the change.

3) Contributing Factors: they are part of the chain of events that caused the chain, but are not the root cause.

4) Root Cause: these are the factors that initiated the chain of events that resulted in the change.

So, segmentation of data obviously helps in Root Cause Analysis.

Segmentation & the ways it is related Root cause analysis:

                         The Purpose of Segmentation is to cut down the number of Suspected process steps or sub components within a system involved in Problem creation. Successful segmentation will narrow down the investigation of fewer models & also it will narrow the original root causes to be analysed further by cutting out the Process steps or components that are not involved in creating the Problem.

 

 The effort in segmentation is to find & learn the data items that can differentiate between the segments involved [Process Steps or Functional Components] by telling if the original Problem source is there or not there. SO it will be useful in the Root cause Analysis to identify the Exact Root cause of the Problem. Wise selection of Data items to collect will cut out irrelevant Sub segments part by part, until isolating the exact segment that is identified as the MOST PROBABLE SOURCE OF THE PROBLEM.

 

This methodology works nicely with the Condition tree, Since Segments arr Natural Building Blocks required to build the Problematic IUI. When running a Segmentation Process, we will try to :

- Narrow down the No. of  Suspect Process Steps into Fewer steps or Even to One Process step that caused the Problem:

Example:

If a visible defect is found at the Late stage of the Process, a visual Check after every process step could find the earliest Process Step in the flow to exhibit this defect. Once this is found, all the subsequent process steps are no longer suspect

 

- Reduce the Number of suspected Components within the System or a Tool that caused the Problem:

Example:

When turning the Key in Our Car ignition, the starter spins, but he engine only roars. They are two main suspect segments, the Fuel & Electrical Ignition System. Each of these two segments could cause the Problem & each contains many Sub components. The Best way to confront the Root cause is by understanding which of these two Major segments is causing the Problem. Checking whether there is Spark in the Cable leading to the Spark Plugs could verify the functionality of the most of the electrical systems. If a Spark fails to appear at the end of the cable, it is necessary to continue to isolate the component [Smaller Segment], within the Electrical system causing the Failure. Suppose if there a Nice & healthy Spark, One should suspect the Problem lies in either in the Fuel System or a small no. of electrical components behind the cable.

 

From the above the Steps & examples we can clearly understand that Segmentation is very helpful to identify the Possible root causes & Select the Most likely factors from that collection. Finally it helps us to classify the Factors & identify the EXACT Root cause among them